Answer:
Z-Mart purchased $3,000 worth of merchandise on credit. Transportation costs were an additional $100, paid cash to the cartage company on delivery. Z-Mart returned $300 worth of merchandise and paid the invoice on time, and took a 2% purchase discount. The amount of this payment was <u>$2744</u>
Explanation:
Purchases excluding freight $3,000
Less:Goods returned -$300
Add:freight charges $100
Net Purchases $2,800
Less:Discount on payment($2,800*2%) -$56
Net cash paid $2,844
Answer:true
Explanation:
To create an atmosphere of easy communication and profit earning
Answer:
The correct answer is letter "C": Accommodating.
Explanation:
By accommodating managers have to adjust their plans according to current situations happening at the workplace. Problematic situations must be solved quickly but they can also represent a chance to spot weaknesses of the organization that should be reviewed. If a method of working shall be modified or if there is a need to use the company's resources to make adjustments on the issue, top managers have the power to do so.
Thus, <em>accommodating will be useful for the COO of Barcelona Restaurants to join his method of working with one of the manager's style.</em>
Answer: $112000
Explanation:
First, we calculate the book value in year 7 which will be:
= Depreciation × Balance life
= $400,000 × 3/10
= $120,000
Then, the cash flow as a result of the transaction will be:
= Asset sale - (Asset - Book value) × Tax rate
= 110000 - [(110000 - 120000) × 20%]
= 110000 - (-2000)
= 110000 + 2000
= 112000
Answer:
Supervised and Unsupervised Learning:
a. Unsupervised learning
b. Supervised learning
3. Supervised learning
4. Unsupervised learning
Explanation:
The key difference between supervised machine learning and unsupervised machine learning is that with supervised machine learning there is a training dataset (labeled data) on which the algorithm is trained to predict patterns. With unsupervised machine learning on the other hand, there is no training data. So, the algorithm discovers patterns on itself without reference to another labeled data or training dataset.
